The plot functions allow you to customize the resulting plots in two ways. First of all, the plots are generated using the ggplot2 library and can hence be modified using the library’s + syntax. Secondly, they can be modified using the plot function’s ... argument.
Using ggplot’s + syntax
CroPlotR generates all plots using the library ggplot2 and they can hence be customized using the library’s + syntax. For instance, in order to change the axis labels or in order to add a line to the graph, one can simply write
library(ggplot2)
plot_soil(soil, type="thickness.mswc") +
# overwrite label on x axis
xlab("My modified label on x axis") +
# add horizontal red line
geom_abline(slope = 3.6, intercept = -90, color = "red")
Using the ... argument with constant values
Plots can also be customized using the plot function’s ... argument. All arguments given here are passed to the ggplot2 geometric function when creating the graph. On the present graph, one might for instance want to change the points’ size and opacity in order to make overplotting visible. To do so, one can simply write
library(ggplot2)
plot_soil(soil, type = "thickness.mswc", size = 2, alpha = 0.3)
Note that this is a special usage of the ... argument since the values of size and alpha are both constant, ie. they don’t make any reference to the data provided.
Using the ... argument with data variables
Even more customisations are possible when using the ... argument in combination with data variables. For instance, one might want to suppress the color scale of the above graph and replace it with a size scale, adding an approriate title to the newly created legend:
library(ggplot2)
plot_soil(soil,
type = "thickness.mswc",
# removing color scale that is usually present on this graph type
colour = 1,
# adding our own size scale to the graph
mapping = aes(size = organic_N_conc)) +
# adding a label for our new size scale
labs(size = "Organic N conc.")
The value assigned to the colour argument in this example is constant and it can hence be passed directly to the plot function as seen before The value of the colour aesthetic, on the other hand, is different for every observation and refers to a data variable—we want it to represent the concentration of organic nitrogen in each soil. Therefore, just as when creating a plot manually with ggplot2, this aesthetic has to be given through ggplot2’s aes function. Within the aes function, we can directly refer to data variable, in this case organic_N_conc. As shown in the example, a collection of (one ore more) aesthetics that is given through the aes function is named mapping within the ... argument.
The next example shows the true power of these customized aesthetics. Here, we want to identify graphically which soils in out data set have a particulary high concentration of organic nitrogen, let’s say more that 0.2 g/g. It is important to note that we have to give the bound of 0.2 together with a unit that is convertible to the unit of organic_N_conc.
library(ggplot2)
library(units)
plot_soil(
soil,
type = "thickness.mswc",
mapping = aes(
colour = ifelse(
organic_N_conc >= set_units(0.2, "g/g"),
"\u2265 0.2 g/g",
"< 0.2 g/g"
)
)
)
